The Challenge
Retail Solutions Group, a mid-market e-commerce company, was struggling with customer support scale. Their support team was fielding hundreds of tickets daily - the majority being repetitive questions about order status, returns, and product availability. Response times had ballooned to 8+ hours during peak periods, and customer satisfaction scores were declining.
Hiring more agents wasn’t sustainable. They needed a smarter solution.
Our Approach
We started by analysing 6 months of support ticket data to understand the query landscape. The findings were clear: 70%+ of incoming queries fell into predictable, repeatable categories that didn’t require human judgement.
Our strategy:
- Phase 1 - Deploy an AI chatbot to handle the high-volume, low-complexity queries immediately
- Phase 2 - Integrate with the existing CRM so the bot has context (order history, customer tier, previous interactions)
- Phase 3 - Build intelligent escalation workflows so complex issues reach the right human agent with full context
The Solution
We built a custom AI chatbot powered by a fine-tuned language model, trained on the company’s actual support data, product catalog, and policies. Unlike off-the-shelf chatbots that give generic responses, this system understands the business.
Key capabilities:
- Natural language understanding - handles spelling mistakes, slang, and multi-part questions
- CRM integration - pulls order status, tracking info, and customer history in real time
- Smart escalation - recognises when a query needs human attention and routes to the right team with full context attached
- Continuous learning - the model improves based on agent feedback and conversation outcomes
The bot was deployed across web chat and integrated with their email support pipeline.
Results
Within 90 days of deployment:
- 65% reduction in response time - average response dropped from 8+ hours to under 3 hours
- 70% of queries handled autonomously - no human intervention required
- 24/7 customer coverage - no more “outside business hours” gaps
- 30% cost reduction - support costs decreased while volume was increasing